diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index cdf6f40a..2b524f0e 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -27,7 +27,7 @@ jobs: - name: Install dependencies run: uv sync --extra test - name: Run tests - run: uv run pytest --cov=chainladder --cov-report=xml + run: uv run pytest --nbmake --cov=chainladder --cov-report=xml - name: Upload coverage to Codecov uses: codecov/codecov-action@v4 with: diff --git a/chainladder/core/dunders.py b/chainladder/core/dunders.py index c7a6eddb..e8799703 100644 --- a/chainladder/core/dunders.py +++ b/chainladder/core/dunders.py @@ -124,17 +124,20 @@ def _prep_columns(self, x, y): else: # Find columns to add to each triangle cols_to_add_to_x = [col for col in y.columns if col not in x.columns] - cols_to_add_to_y = [col for col in x.columns if col not in y.columns] - - # Create new columns only if necessary + cols_to_add_to_y = [col for col in x.columns if col not in y.columns] + + # Start with case with no new columns, y simply has a different order + new_x_cols = list(x.columns) + + # Then, if there are new columns, add them. if cols_to_add_to_x: new_x_cols = list(x.columns) + list(cols_to_add_to_x) x = x.reindex(columns=new_x_cols, fill_value=0) - + if cols_to_add_to_y: new_y_cols = list(y.columns) + list(cols_to_add_to_y) y = y.reindex(columns=new_y_cols, fill_value=0) - + # Ensure both triangles have the same column order x = x[new_x_cols] y = y[new_x_cols] diff --git a/chainladder/core/pandas.py b/chainladder/core/pandas.py index 8c90e090..50179246 100644 --- a/chainladder/core/pandas.py +++ b/chainladder/core/pandas.py @@ -18,7 +18,12 @@ if TYPE_CHECKING: from chainladder import Triangle from collections.abc import Callable + from numpy import ndarray from numpy.typing import ArrayLike + from pandas import ( + DataFrame, + Series + ) from types import ModuleType from typing import ( Literal, @@ -52,8 +57,14 @@ def __getitem__(self, key): class TrianglePandas: - def to_frame(self, origin_as_datetime=True, keepdims=False, - implicit_axis=False, *args, **kwargs): + def to_frame( + self, + origin_as_datetime: bool = True, + keepdims: bool = False, + implicit_axis: bool = False, + *args, + **kwargs + ) -> DataFrame | Series: """ Converts a triangle to a pandas.DataFrame. Parameters ---------- @@ -69,55 +80,58 @@ def to_frame(self, origin_as_datetime=True, keepdims=False, valuation axis in addition to the origin and development. Returns ------- - pandas.DataFrame representation of the Triangle. + DataFrame or Series representation of the Triangle. """ - axes = [num for num, item in enumerate(self.shape) if item > 1] + # Identify the axes that increase the dimensionality of the triangle, i.e., those whose length is > 1. + axes: list[int] = [num for num, item in enumerate(self.shape) if item > 1] + + # Long format. if keepdims: - is_val_tri = self.is_val_tri - obj = self.val_to_dev().set_backend("sparse") - out = pd.DataFrame(obj.index.iloc[obj.values.coords[0]]) - out["columns"] = obj.columns[obj.values.coords[1]] - missing_cols = list(set(self.columns) - set(out['columns'])) + is_val_tri: bool = self.is_val_tri + obj: Triangle = self.val_to_dev().set_backend("sparse") + out: DataFrame = pd.DataFrame(obj.index.iloc[obj.values.coords[0]]) + out["columns"]: Series = obj.columns[obj.values.coords[1]] + missing_cols: list = list(set(self.columns) - set(out['columns'])) if origin_as_datetime: - out["origin"] = obj.odims[obj.values.coords[2]] + out["origin"]: Series = obj.odims[obj.values.coords[2]] else: - out["origin"] = obj.origin[obj.values.coords[2]] - out["development"] = obj.ddims[obj.values.coords[3]] - out["values"] = obj.values.data - out = pd.pivot_table( + out["origin"]: Series = obj.origin[obj.values.coords[2]] + out["development"]: Series = obj.ddims[obj.values.coords[3]] + out["values"]: Series = obj.values.data + out: DataFrame = pd.pivot_table( out, index=obj.key_labels + ["origin", "development"], columns="columns" ) - out = out.reset_index().set_index(obj.key_labels) + out: DataFrame = out.reset_index().set_index(obj.key_labels) out.columns = ["origin", "development"] + list( out.columns.get_level_values(1)[2:] ) - valuation = pd.DataFrame( + valuation: DataFrame = pd.DataFrame( obj.valuation.values.reshape(obj.shape[-2:], order='F'), index=obj.odims if origin_as_datetime else obj.origin, columns=obj.ddims ).unstack().rename('valuation').reset_index().rename( columns={'level_0': 'development', 'level_1': 'origin'}) - val_dict = dict(zip(list(zip( + val_dict: dict = dict(zip(list(zip( valuation['origin'], valuation['development'])), valuation['valuation'])) if len(out) > 0: - out['valuation'] = out.apply( + out['valuation']: Series = out.apply( lambda x: val_dict[(x['origin'], x['development'])], axis=1) else: - out['valuation'] = self.valuation_date - col_order = list(self.columns) + out['valuation']: Series = self.valuation_date + col_order: list = list(self.columns) if implicit_axis: - col_order = ['origin', 'development', 'valuation'] + col_order + col_order: list = ['origin', 'development', 'valuation'] + col_order else: if is_val_tri: - col_order = ['origin', 'valuation'] + col_order + col_order: list = ['origin', 'valuation'] + col_order else: - col_order = ['origin', 'development'] + col_order + col_order: list = ['origin', 'development'] + col_order for col in set(missing_cols) - self.virtual_columns.columns.keys(): - out[col] = np.nan + out[col]: Series = np.nan for col in set(missing_cols).intersection(self.virtual_columns.columns.keys()): out[col] = out.fillna(0).apply(self.virtual_columns.columns[col], 1) out.loc[out[col] == 0, col] = np.nan @@ -126,35 +140,40 @@ def to_frame(self, origin_as_datetime=True, keepdims=False, # keepdims = False else: + # Case when there is a single triangle, for a single segment. if self.shape[:2] == (1, 1): return self._repr_format(origin_as_datetime) - + # Case when triangle is multidimensional but is of unusual shape, such as a collection of latest diagonals. elif len(axes) in [1, 2]: - tri = np.squeeze(self.set_backend("numpy").values) - axes_lookup = { + tri: ndarray = np.squeeze(self.set_backend("numpy").values) + axes_lookup: dict = { 0: self.kdims, 1: self.vdims, 2: self.origin, 3: self.development, } + # Set the index to be key dimension if the key dimension is greater than length 1. if axes[0] == 0: idx = self.index.set_index(self.key_labels).index + # Otherwise, find the axis that is greater than length 0 and set that to be the index. else: idx = axes_lookup[axes[0]] if len(axes) == 1: return pd.Series(tri, index=idx).fillna(0) - - elif len(axes) == 2: + # Case len(axes) == 2. + else: return pd.DataFrame( tri, index=idx, columns=axes_lookup[axes[1]] ).fillna(0) - + # Multidimensional triangles, return DataFrame in long form. else: return self.to_frame( - origin_as_datetime=origin_as_datetime, keepdims=True, - implicit_axis=implicit_axis) + origin_as_datetime=origin_as_datetime, + keepdims=True, + implicit_axis=implicit_axis + ) def plot(self, *args, **kwargs): """Passthrough of pandas functionality""" @@ -410,7 +429,7 @@ def agg_func( axis: str | int | None = None, *args, **kwargs - ) -> Triangle | np.ndarray: + ) -> Triangle | ndarray: """ Applies the aggregation function specified by k from the outer function. diff --git a/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb b/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb index 0c35d759..f98b7079 100644 --- a/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb +++ b/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb @@ -809,6 +809,10 @@ "language": "python", "name": "python3" }, + "execution": { + "allow_errors": true, + "timeout": 300 + }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/docs/getting_started/tutorials/data-tutorial.ipynb b/docs/getting_started/tutorials/data-tutorial.ipynb index deb54eb2..8f414fd3 100644 --- a/docs/getting_started/tutorials/data-tutorial.ipynb +++ b/docs/getting_started/tutorials/data-tutorial.ipynb @@ -13,24 +13,28 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": {}, + "execution_count": 1, + "metadata": { + "jupyter": { + "is_executing": true + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pandas: 2.3.3\n", - "numpy: 2.3.3\n", - "chainladder: 0.8.25\n" + "numpy: 1.26.4\n", + "chainladder: 0.8.23\n" ] } ], "source": [ "# Black linter, optional\n", - "import jupyter_black as jb\n", + "# import jupyter_black as jb\n", "\n", - "jb.load()\n", + "# jb.load()\n", "\n", "import pandas as pd\n", "import numpy as np\n", @@ -72,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -269,7 +273,7 @@ "1990-01-01 NaN NaN NaN " ] }, - "execution_count": 25, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -288,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -396,7 +400,7 @@ "9 12 1990-01-01 2063.0" ] }, - "execution_count": 26, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -415,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -488,7 +492,7 @@ "4 12 1985-01-01 1092.0" ] }, - "execution_count": 27, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -507,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -586,7 +590,7 @@ "4 12 1985-01-01 1092.0 1985" ] }, - "execution_count": 28, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -605,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -775,7 +779,7 @@ "1990 2063.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" ] }, - "execution_count": 29, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -800,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -846,7 +850,7 @@ "Columns: [reportedCount, closedPaidCount, Paid, Incurred]" ] }, - "execution_count": 30, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -865,19 +869,19 @@ }, { "cell_type": "code", - 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" ], "text/plain": [ "" ] }, - "execution_count": 34, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -992,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1207,7 +1211,7 @@ "[160 rows x 10 columns]" ] }, - "execution_count": 35, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1228,31 +1232,31 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 36, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1274,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1284,31 +1288,31 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 38, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1341,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1358,7 +1362,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1404,7 +1408,7 @@ "Columns: [Paid, reportedCount]" ] }, - "execution_count": 40, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1423,22 +1427,22 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 41, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1471,7 +1475,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1482,7 +1486,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1491,13 +1495,13 @@ "" ] }, - "execution_count": 43, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1522,7 +1526,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1533,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1542,13 +1546,13 @@ "" ] }, - "execution_count": 45, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -1581,7 +1585,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/getting_started/tutorials/deterministic-tutorial.ipynb b/docs/getting_started/tutorials/deterministic-tutorial.ipynb index f831b8fd..dc2cce7e 100644 --- a/docs/getting_started/tutorials/deterministic-tutorial.ipynb +++ b/docs/getting_started/tutorials/deterministic-tutorial.ipynb @@ -13,23 +13,16 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 2.3.3\n", - "numpy: 2.3.3\n", - "chainladder: 0.8.25\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:07:38.785420Z", + "start_time": "2026-01-28T04:07:36.859133Z" } - ], + }, "source": [ "# Black linter, optional\n", - "import jupyter_black as jb\n", - "jb.load()\n", + "# import jupyter_black as jb\n", + "# jb.load()\n", "\n", "import pandas as pd\n", "import numpy as np\n", @@ -39,7 +32,19 @@ "print(\"pandas: \" + pd.__version__)\n", "print(\"numpy: \" + np.__version__)\n", "print(\"chainladder: \" + cl.__version__)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 2.3.3\n", + "numpy: 2.2.6\n", + "chainladder: 0.8.26\n" + ] + } + ], + "execution_count": 1 }, { "cell_type": "markdown", @@ -62,16 +67,21 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:07:39.008988Z", + "start_time": "2026-01-28T04:07:38.956226Z" + } + }, "source": [ "genins = cl.load_sample(\"genins\")\n", "\n", "genins_dev = cl.Pipeline(\n", " [(\"dev\", cl.Development()), (\"tail\", cl.TailCurve())]\n", ").fit_transform(genins)" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", @@ -82,11 +92,32 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:07:39.151516Z", + "start_time": "2026-01-28T04:07:39.136034Z" + } + }, + "source": [ + "genins_model = cl.Chainladder().fit(genins_dev)\n", + "genins_model.ultimate_" + ], "outputs": [ { "data": { + "text/plain": [ + " 2261\n", + "2001 4.016553e+06\n", + "2002 5.594009e+06\n", + "2003 5.537497e+06\n", + "2004 5.454190e+06\n", + "2005 5.001513e+06\n", + "2006 5.261947e+06\n", + "2007 5.827759e+06\n", + "2008 6.984945e+06\n", + "2009 5.808708e+06\n", + "2010 5.116430e+06" + ], "text/html": [ "\n", " \n", @@ -138,19 +169,6 @@ " \n", " \n", "
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" - ], - "text/plain": [ - " 2011\n", - "2001 4.660832e+04\n", - "2002 9.463381e+04\n", - "2003 3.758335e+05\n", - "2004 2.471900e+05\n", - "2005 3.341481e+05\n", - "2006 3.832866e+05\n", - "2007 6.055481e+05\n", - "2008 1.310258e+06\n", - "2009 1.018834e+06\n", - "2010 8.568035e+05" ] }, "execution_count": 15, @@ -2442,10 +2520,7 @@ "output_type": "execute_result" } ], - "source": [ - "cal_yr_ibnr = genins_model.full_triangle_.dev_to_val().cum_to_incr()\n", - "cal_yr_ibnr[cal_yr_ibnr.valuation.year == 2011]" - ] + "execution_count": 15 }, { "cell_type": "markdown", @@ -2469,11 +2544,32 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:07:44.067751Z", + "start_time": "2026-01-28T04:07:44.060952Z" + } + }, + "source": [ + "expected_loss_apriori = genins_model.ultimate_ * 0 + genins_model.ultimate_.mean()\n", + "expected_loss_apriori" + ], "outputs": [ { "data": { + "text/plain": [ + " 2261\n", + "2001 5.460355e+06\n", + "2002 5.460355e+06\n", + "2003 5.460355e+06\n", + "2004 5.460355e+06\n", + "2005 5.460355e+06\n", + "2006 5.460355e+06\n", + "2007 5.460355e+06\n", + "2008 5.460355e+06\n", + "2009 5.460355e+06\n", + "2010 5.460355e+06" + ], "text/html": [ "\n", " \n", @@ -2525,19 +2621,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 2261\n", - "2001 5.460355e+06\n", - "2002 5.460355e+06\n", - "2003 5.460355e+06\n", - "2004 5.460355e+06\n", - "2005 5.460355e+06\n", - "2006 5.460355e+06\n", - "2007 5.460355e+06\n", - "2008 5.460355e+06\n", - "2009 5.460355e+06\n", - "2010 5.460355e+06" ] }, "execution_count": 16, @@ -2545,10 +2628,7 @@ "output_type": "execute_result" } ], - "source": [ - "expected_loss_apriori = genins_model.ultimate_ * 0 + genins_model.ultimate_.mean()\n", - "expected_loss_apriori" - ] + "execution_count": 16 }, { "cell_type": "markdown", @@ -2559,11 +2639,34 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:07:44.389374Z", + "start_time": "2026-01-28T04:07:44.348386Z" + } + }, + "source": [ + "EL_model = cl.ExpectedLoss(apriori=0.95).fit(\n", + " genins, sample_weight=expected_loss_apriori\n", + ")\n", + "EL_model.ultimate_" + ], + "outputs": [ { "data": { + "text/plain": [ + " 2261\n", + "2001 5.187337e+06\n", + "2002 5.187337e+06\n", + "2003 5.187337e+06\n", + "2004 5.187337e+06\n", + "2005 5.187337e+06\n", + "2006 5.187337e+06\n", + "2007 5.187337e+06\n", + "2008 5.187337e+06\n", + "2009 5.187337e+06\n", + "2010 5.187337e+06" + ], "text/html": [ "\n", " \n", @@ -2615,19 +2718,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 2261\n", - "2001 5.187337e+06\n", - "2002 5.187337e+06\n", - "2003 5.187337e+06\n", - "2004 5.187337e+06\n", - "2005 5.187337e+06\n", - "2006 5.187337e+06\n", - "2007 5.187337e+06\n", - "2008 5.187337e+06\n", - "2009 5.187337e+06\n", - "2010 5.187337e+06" ] }, "execution_count": 17, @@ -2635,12 +2725,7 @@ "output_type": "execute_result" } ], - "source": [ - "EL_model = cl.ExpectedLoss(apriori=0.95).fit(\n", - " genins, sample_weight=expected_loss_apriori\n", - ")\n", - "EL_model.ultimate_" - ] + "execution_count": 17 }, { "cell_type": "markdown", @@ -2675,11 +2760,26 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:07:45.022767Z", + "start_time": "2026-01-28T04:07:44.654467Z" + } + }, + "source": [ + "comauto = cl.load_sample(\"clrd\").groupby(\"LOB\").sum().loc[\"comauto\"]\n", + "\n", + "bf_model = cl.BornhuetterFerguson(apriori=0.75)\n", + "bf_model.fit(\n", + " comauto[\"CumPaidLoss\"], sample_weight=comauto[\"EarnedPremNet\"].latest_diagonal\n", + ")" + ], "outputs": [ { "data": { + "text/plain": [ + "BornhuetterFerguson(apriori=0.75)" + ], "text/html": [ "\n", - "\n", + "
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 12-2424-3636-4848-6060-7272-8484-9696-108108-12012-2424-3636-4848-6060-7272-8484-9696-108108-120
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20044.56801.54711.71181.07251.08741.047120044.56801.54711.71181.07251.08741.0471
20052.56421.87301.36151.17421.138320052.56421.87301.36151.17421.1383
20063.36561.63571.36921.236420063.36561.63571.36921.2364
20072.92281.87811.439420072.92281.87811.4394
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20093.619220093.6192
\n" - ], - "text/plain": [ - "" ] }, "execution_count": 12, @@ -1072,17 +1134,27 @@ "output_type": "execute_result" } ], - "source": [ - "genins.age_to_age.heatmap()" - ] + "execution_count": 12 }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:13.198090Z", + "start_time": "2026-01-28T04:15:13.180004Z" + } + }, + "source": [ + "vol = cl.Development(average=\"volume\").fit(genins).ldf_\n", + "vol" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ], "text/html": [ "\n", " \n", @@ -1114,10 +1186,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" ] }, "execution_count": 13, @@ -1125,19 +1193,28 @@ "output_type": "execute_result" } ], - "source": [ - "vol = cl.Development(average=\"volume\").fit(genins).ldf_\n", - "vol" - ] + "execution_count": 13 }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:13.281249Z", + "start_time": "2026-01-28T04:15:13.265907Z" + } + }, + "source": [ + "sim = cl.Development(average=\"simple\").fit(genins).ldf_\n", + "sim" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.566143 1.745557 1.451961 1.180984 1.111247 1.084818 1.052739 1.074753 1.017725" + ], + "text/html": [ "\n", " \n", " \n", @@ -1168,10 +1245,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.566143 1.745557 1.451961 1.180984 1.111247 1.084818 1.052739 1.074753 1.017725" ] }, "execution_count": 14, @@ -1179,10 +1252,7 @@ "output_type": "execute_result" } ], - "source": [ - "sim = cl.Development(average=\"simple\").fit(genins).ldf_\n", - "sim" - ] + "execution_count": 14 }, { "cell_type": "markdown", @@ -1193,8 +1263,17 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:13.381607Z", + "start_time": "2026-01-28T04:15:13.373885Z" + } + }, + "source": [ + "print(\"LDF Type: \", type(vol))\n", + "print(\"Difference between volume and simple average:\")\n", + "vol - sim" + ], "outputs": [ { "name": "stdout", @@ -1206,6 +1285,10 @@ }, { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) -0.075536 0.001776 0.005452 -0.007132 -0.007423 0.001452 0.001135 0.001802 NaN" + ], "text/html": [ "\n", " \n", @@ -1237,10 +1320,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) -0.075536 0.001776 0.005452 -0.007132 -0.007423 0.001452 0.001135 0.001802 NaN" ] }, "execution_count": 15, @@ -1248,11 +1327,7 @@ "output_type": "execute_result" } ], - "source": [ - "print(\"LDF Type: \", type(vol))\n", - "print(\"Difference between volume and simple average:\")\n", - "vol - sim" - ] + "execution_count": 15 }, { "cell_type": "markdown", @@ -1263,11 +1338,22 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:13.621381Z", + "start_time": "2026-01-28T04:15:13.605546Z" + } + }, + "source": [ + "cl.Development(average=[\"volume\", \"simple\", \"regression\"] * 3).fit(genins).ldf_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.745557 1.461852 1.173852 1.111247 1.087341 1.053874 1.074753 1.017725" + ], "text/html": [ "\n", " \n", @@ -1299,10 +1385,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.745557 1.461852 1.173852 1.111247 1.087341 1.053874 1.074753 1.017725" ] }, "execution_count": 16, @@ -1310,9 +1392,7 @@ "output_type": "execute_result" } ], - "source": [ - "cl.Development(average=[\"volume\", \"simple\", \"regression\"] * 3).fit(genins).ldf_" - ] + "execution_count": 16 }, { "cell_type": "markdown", @@ -1323,11 +1403,22 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:13.703293Z", + "start_time": "2026-01-28T04:15:13.687120Z" + } + }, + "source": [ + "cl.Development(average=[\"volume\"] + [\"simple\"] * 5 + [\"volume\"] * 3).fit(genins).ldf_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.745557 1.451961 1.180984 1.111247 1.084818 1.053874 1.076555 1.017725" + ], "text/html": [ "\n", " \n", @@ -1359,10 +1450,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.745557 1.451961 1.180984 1.111247 1.084818 1.053874 1.076555 1.017725" ] }, "execution_count": 17, @@ -1370,9 +1457,7 @@ "output_type": "execute_result" } ], - "source": [ - "cl.Development(average=[\"volume\"] + [\"simple\"] * 5 + [\"volume\"] * 3).fit(genins).ldf_" - ] + "execution_count": 17 }, { "cell_type": "markdown", @@ -1390,11 +1475,22 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:13.856040Z", + "start_time": "2026-01-28T04:15:13.839053Z" + } + }, + "source": [ + "cl.Development().fit(genins).ldf_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ], "text/html": [ "\n", " \n", @@ -1426,10 +1522,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" ] }, "execution_count": 18, @@ -1437,17 +1529,26 @@ "output_type": "execute_result" } ], - "source": [ - "cl.Development().fit(genins).ldf_" - ] + "execution_count": 18 }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:14.003290Z", + "start_time": "2026-01-28T04:15:13.986528Z" + } + }, + "source": [ + "cl.Development(n_periods=-1).fit(genins).ldf_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" + ], "text/html": [ "\n", " \n", @@ -1479,10 +1580,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 3.490607 1.747333 1.457413 1.173852 1.103824 1.086269 1.053874 1.076555 1.017725" ] }, "execution_count": 19, @@ -1490,17 +1587,26 @@ "output_type": "execute_result" } ], - "source": [ - "cl.Development(n_periods=-1).fit(genins).ldf_" - ] + "execution_count": 19 }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:14.131683Z", + "start_time": "2026-01-28T04:15:14.114007Z" + } + }, + "source": [ + "cl.Development(n_periods=3).fit(genins).ldf_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 3.460401 1.846507 1.392009 1.153852 1.084915 1.097355 1.053874 1.076555 1.017725" + ], "text/html": [ "\n", " \n", @@ -1532,10 +1638,6 @@ " \n", " \n", "
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" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "2001 3.143200 1.542806 1.278299 NaN 1.209207 1.044079 1.040374 1.063009 1.017725\n", - "2002 3.510582 1.755493 1.545286 1.132926 1.084493 1.128106 1.057268 1.086496 NaN\n", - "2003 4.448450 1.716718 1.458257 1.232079 1.036860 1.120010 1.060577 NaN NaN\n", - "2004 NaN 1.547052 NaN 1.072518 1.087360 1.047076 NaN NaN NaN\n", - "2005 2.564198 1.872956 1.361545 1.174217 1.138315 NaN NaN NaN NaN\n", - "2006 3.365588 1.635679 1.369162 1.236443 NaN NaN NaN NaN NaN\n", - "2007 2.922798 1.878099 1.439393 NaN NaN NaN NaN NaN NaN\n", - "2008 3.953288 NaN NaN NaN NaN NaN NaN NaN NaN\n", - "2009 3.619179 NaN NaN NaN NaN NaN NaN NaN NaN" ] }, "execution_count": 32, @@ -2688,9 +2850,7 @@ "output_type": "execute_result" } ], - "source": [ - "transformed_triangle.link_ratio" - ] + "execution_count": 32 }, { "cell_type": "markdown", @@ -2701,172 +2861,182 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:16.086791Z", + "start_time": "2026-01-28T04:15:16.075657Z" + } + }, + "source": [ + "transformed_triangle.link_ratio.heatmap()" + ], "outputs": [ { "data": { + "text/plain": [ + "" + ], "text/html": [ "\n", - "\n", + "
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\n" - ], - "text/plain": [ - "" ] }, "execution_count": 33, @@ -2921,14 +3088,20 @@ "output_type": "execute_result" } ], - "source": [ - "transformed_triangle.link_ratio.heatmap()" - ] + "execution_count": 33 }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:16.330586Z", + "start_time": "2026-01-28T04:15:16.320447Z" + } + }, + "source": [ + "print(type(transformed_triangle))\n", + "transformed_triangle.latest_diagonal" + ], "outputs": [ { "name": "stdout", @@ -2939,6 +3112,19 @@ }, { "data": { + "text/plain": [ + " 2010\n", + "2001 3901463.0\n", + "2002 5339085.0\n", + "2003 4909315.0\n", + "2004 4588268.0\n", + "2005 3873311.0\n", + "2006 3691712.0\n", + "2007 3483130.0\n", + "2008 2864498.0\n", + "2009 1363294.0\n", + "2010 344014.0" + ], "text/html": [ "\n", " \n", @@ -2990,19 +3176,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 2010\n", - "2001 3901463.0\n", - "2002 5339085.0\n", - "2003 4909315.0\n", - "2004 4588268.0\n", - "2005 3873311.0\n", - "2006 3691712.0\n", - "2007 3483130.0\n", - "2008 2864498.0\n", - "2009 1363294.0\n", - "2010 344014.0" ] }, "execution_count": 34, @@ -3010,10 +3183,7 @@ "output_type": "execute_result" } ], - "source": [ - "print(type(transformed_triangle))\n", - "transformed_triangle.latest_diagonal" - ] + "execution_count": 34 }, { "cell_type": "markdown", @@ -3024,11 +3194,22 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:16.464120Z", + "start_time": "2026-01-28T04:15:16.455056Z" + } + }, + "source": [ + "transformed_triangle.cdf_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult\n", + "(All) 13.136729 3.886978 2.28089 1.61311 1.384499 1.254276 1.154664 1.095637 1.017725" + ], "text/html": [ "\n", " \n", @@ -3060,10 +3241,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-Ult 24-Ult 36-Ult 48-Ult 60-Ult 72-Ult 84-Ult 96-Ult 108-Ult\n", - "(All) 13.136729 3.886978 2.28089 1.61311 1.384499 1.254276 1.154664 1.095637 1.017725" ] }, "execution_count": 35, @@ -3071,9 +3248,7 @@ "output_type": "execute_result" } ], - "source": [ - "transformed_triangle.cdf_" - ] + "execution_count": 35 }, { "cell_type": "markdown", @@ -3084,8 +3259,15 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:16.606871Z", + "start_time": "2026-01-28T04:15:16.586399Z" + } + }, + "source": [ + "cl.Development().fit_transform(genins) == cl.Development().fit(genins).transform(genins)" + ], "outputs": [ { "data": { @@ -3098,9 +3280,7 @@ "output_type": "execute_result" } ], - "source": [ - "cl.Development().fit_transform(genins) == cl.Development().fit(genins).transform(genins)" - ] + "execution_count": 36 }, { "cell_type": "markdown", @@ -3115,11 +3295,32 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:16.986662Z", + "start_time": "2026-01-28T04:15:16.717007Z" + } + }, + "source": [ + "clrd = cl.load_sample(\"clrd\")\n", + "comauto = clrd[clrd[\"LOB\"] == \"comauto\"][\"CumPaidLoss\"]\n", + "\n", + "comauto_industry = comauto.sum()\n", + "industry_dev = cl.Development().fit(comauto_industry)\n", + "\n", + "industry_dev.transform(comauto)" + ], "outputs": [ { "data": { + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (157, 1, 10, 10)\n", + "Index: [GRNAME, LOB]\n", + "Columns: [CumPaidLoss]" + ], "text/html": [ "\n", " \n", @@ -3151,14 +3352,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (157, 1, 10, 10)\n", - "Index: [GRNAME, LOB]\n", - "Columns: [CumPaidLoss]" ] }, "execution_count": 37, @@ -3166,15 +3359,7 @@ "output_type": "execute_result" } ], - "source": [ - "clrd = cl.load_sample(\"clrd\")\n", - "comauto = clrd[clrd[\"LOB\"] == \"comauto\"][\"CumPaidLoss\"]\n", - "\n", - "comauto_industry = comauto.sum()\n", - "industry_dev = cl.Development().fit(comauto_industry)\n", - "\n", - "industry_dev.transform(comauto)" - ] + "execution_count": 37 }, { "cell_type": "markdown", @@ -3187,8 +3372,17 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:17.314961Z", + "start_time": "2026-01-28T04:15:17.044935Z" + } + }, + "source": [ + "clrd = cl.load_sample(\"clrd\").groupby(\"LOB\").sum()[\"CumPaidLoss\"]\n", + "print(\"Fitting to \" + str(len(clrd.index)) + \" industries simultaneously.\")\n", + "cl.Development().fit_transform(clrd).cdf_" + ], "outputs": [ { "name": "stdout", @@ -3199,6 +3393,14 @@ }, { "data": { + "text/plain": [ + " Triangle Summary\n", + "Valuation: 2261-12\n", + "Grain: OYDY\n", + "Shape: (6, 1, 1, 9)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss]" + ], "text/html": [ "\n", " \n", @@ -3230,14 +3432,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 2261-12\n", - "Grain: OYDY\n", - "Shape: (6, 1, 1, 9)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" ] }, "execution_count": 38, @@ -3245,11 +3439,7 @@ "output_type": "execute_result" } ], - "source": [ - "clrd = cl.load_sample(\"clrd\").groupby(\"LOB\").sum()[\"CumPaidLoss\"]\n", - "print(\"Fitting to \" + str(len(clrd.index)) + \" industries simultaneously.\")\n", - "cl.Development().fit_transform(clrd).cdf_" - ] + "execution_count": 38 }, { "cell_type": "markdown", @@ -3260,8 +3450,17 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:15:17.446081Z", + "start_time": "2026-01-28T04:15:17.411603Z" + } + }, + "source": [ + "print(cl.Development(average=\"simple\").fit(clrd.loc[\"wkcomp\"]))\n", + "print(cl.Development(n_periods=4).fit(clrd.loc[\"ppauto\"]))\n", + "print(cl.Development(average=\"regression\", n_periods=6).fit(clrd.loc[\"comauto\"]))" + ], "outputs": [ { "name": "stdout", @@ -3273,11 +3472,7 @@ ] } ], - "source": [ - "print(cl.Development(average=\"simple\").fit(clrd.loc[\"wkcomp\"]))\n", - "print(cl.Development(n_periods=4).fit(clrd.loc[\"ppauto\"]))\n", - "print(cl.Development(average=\"regression\", n_periods=6).fit(clrd.loc[\"comauto\"]))" - ] + "execution_count": 39 } ], "metadata": { diff --git a/docs/getting_started/tutorials/stochastic-tutorial.ipynb b/docs/getting_started/tutorials/stochastic-tutorial.ipynb index a560e08e..d27d86b1 100644 --- a/docs/getting_started/tutorials/stochastic-tutorial.ipynb +++ b/docs/getting_started/tutorials/stochastic-tutorial.ipynb @@ -13,23 +13,16 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pandas: 2.3.3\n", - "numpy: 2.3.3\n", - "chainladder: 0.8.25\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.146577Z", + "start_time": "2026-01-28T04:16:41.842692Z" } - ], + }, "source": [ "# Black linter, optional\n", - "import jupyter_black as jb\n", - "jb.load()\n", + "# import jupyter_black as jb\n", + "# jb.load()\n", "\n", "import pandas as pd\n", "import numpy as np\n", @@ -40,7 +33,19 @@ "print(\"pandas: \" + pd.__version__)\n", "print(\"numpy: \" + np.__version__)\n", "print(\"chainladder: \" + cl.__version__)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas: 2.3.3\n", + "numpy: 2.2.6\n", + "chainladder: 0.8.26\n" + ] + } + ], + "execution_count": 1 }, { "cell_type": "markdown", @@ -61,20 +66,12 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "np.True_" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.585032Z", + "start_time": "2026-01-28T04:16:43.257746Z" } - ], + }, "source": [ "clrd = (\n", " cl.load_sample(\"clrd\")\n", @@ -86,7 +83,20 @@ "cl.Chainladder().fit(clrd[\"CumPaidLoss\"]).ultimate_ == cl.MackChainladder().fit(\n", " clrd[\"CumPaidLoss\"]\n", ").ultimate_" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.True_" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 2 }, { "cell_type": "markdown", @@ -97,12 +107,17 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.647022Z", + "start_time": "2026-01-28T04:16:43.598508Z" + } + }, "source": [ "mack = cl.MackChainladder().fit(clrd[\"CumPaidLoss\"])" - ] + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "markdown", @@ -121,11 +136,31 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.700990Z", + "start_time": "2026-01-28T04:16:43.693923Z" + } + }, + "source": [ + "clrd_first_lags = clrd[clrd.development <= 24][clrd.origin < \"1997\"][\"CumPaidLoss\"]\n", + "clrd_first_lags" + ], "outputs": [ { "data": { + "text/plain": [ + " 12 24\n", + "1988 285804.0 638532.0\n", + "1989 307720.0 684140.0\n", + "1990 320124.0 757479.0\n", + "1991 347417.0 793749.0\n", + "1992 342982.0 781402.0\n", + "1993 342385.0 743433.0\n", + "1994 351060.0 750392.0\n", + "1995 343841.0 768575.0\n", + "1996 381484.0 736040.0" + ], "text/html": [ "\n", " \n", @@ -183,18 +218,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12 24\n", - "1988 285804.0 638532.0\n", - "1989 307720.0 684140.0\n", - "1990 320124.0 757479.0\n", - "1991 347417.0 793749.0\n", - "1992 342982.0 781402.0\n", - "1993 342385.0 743433.0\n", - "1994 351060.0 750392.0\n", - "1995 343841.0 768575.0\n", - "1996 381484.0 736040.0" ] }, "execution_count": 4, @@ -202,10 +225,7 @@ "output_type": "execute_result" } ], - "source": [ - "clrd_first_lags = clrd[clrd.development <= 24][clrd.origin < \"1997\"][\"CumPaidLoss\"]\n", - "clrd_first_lags" - ] + "execution_count": 4 }, { "cell_type": "markdown", @@ -216,14 +236,21 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.797670Z", + "start_time": "2026-01-28T04:16:43.793413Z" + } + }, + "source": [ + "clrd_first_lags.link_ratio.to_frame(origin_as_datetime=True).mean()[0]" + ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\somra\\AppData\\Local\\Temp\\ipykernel_16868\\1055289506.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + "/tmp/ipykernel_1041314/508033830.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " clrd_first_lags.link_ratio.to_frame(origin_as_datetime=True).mean()[0]\n" ] }, @@ -238,9 +265,7 @@ "output_type": "execute_result" } ], - "source": [ - "clrd_first_lags.link_ratio.to_frame(origin_as_datetime=True).mean()[0]" - ] + "execution_count": 5 }, { "cell_type": "markdown", @@ -251,8 +276,17 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.911843Z", + "start_time": "2026-01-28T04:16:43.893014Z" + } + }, + "source": [ + "cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"]).ldf_.to_frame(\n", + " origin_as_datetime=False\n", + ").values[0, 0]" + ], "outputs": [ { "data": { @@ -265,11 +299,7 @@ "output_type": "execute_result" } ], - "source": [ - "cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"]).ldf_.to_frame(\n", - " origin_as_datetime=False\n", - ").values[0, 0]" - ] + "execution_count": 6 }, { "cell_type": "markdown", @@ -285,11 +315,61 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:43.995558Z", + "start_time": "2026-01-28T04:16:43.966625Z" + } + }, + "source": [ + "y = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 1]\n", + "x = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 0]\n", + "\n", + "model = sm.WLS(y, x, weights=(1 / x) ** 2)\n", + "results = model.fit()\n", + "results.summary()" + ], "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/Repos/chainladder-python/.venv/lib/python3.10/site-packages/scipy/stats/_axis_nan_policy.py:430: UserWarning: `kurtosistest` p-value may be inaccurate with fewer than 20 observations; only n=9 observations were given.\n", + " return hypotest_fun_in(*args, **kwds)\n" + ] + }, { "data": { + "text/plain": [ + "\n", + "\"\"\"\n", + " WLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.997\n", + "Model: WLS Adj. R-squared (uncentered): 0.997\n", + "Method: Least Squares F-statistic: 2887.\n", + "Date: Tue, 27 Jan 2026 Prob (F-statistic): 1.60e-11\n", + "Time: 22:16:43 Log-Likelihood: -107.89\n", + "No. Observations: 9 AIC: 217.8\n", + "Df Residuals: 8 BIC: 218.0\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 2.2067 0.041 53.735 0.000 2.112 2.301\n", + "==============================================================================\n", + "Omnibus: 7.448 Durbin-Watson: 1.177\n", + "Prob(Omnibus): 0.024 Jarque-Bera (JB): 2.533\n", + "Skew: -1.187 Prob(JB): 0.282\n", + "Kurtosis: 4.058 Cond. No. 1.00\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ], "text/html": [ "\n", "\n", @@ -303,10 +383,10 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -344,85 +424,14 @@ "\n", "
WLS Regression Results
Method: Least Squares F-statistic: 2887.
Date: Sat, 18 Oct 2025 Prob (F-statistic): 1.60e-11Date: Tue, 27 Jan 2026 Prob (F-statistic): 1.60e-11
Time: 19:03:41 Log-Likelihood: -107.89Time: 22:16:43 Log-Likelihood: -107.89
No. Observations: 9 AIC: 217.8


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "text/latex": [ - "\\begin{center}\n", - "\\begin{tabular}{lclc}\n", - "\\toprule\n", - "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.997 \\\\\n", - "\\textbf{Model:} & WLS & \\textbf{ Adj. R-squared (uncentered):} & 0.997 \\\\\n", - "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2887. \\\\\n", - "\\textbf{Date:} & Sat, 18 Oct 2025 & \\textbf{ Prob (F-statistic):} & 1.60e-11 \\\\\n", - "\\textbf{Time:} & 19:03:41 & \\textbf{ Log-Likelihood: } & -107.89 \\\\\n", - "\\textbf{No. Observations:} & 9 & \\textbf{ AIC: } & 217.8 \\\\\n", - "\\textbf{Df Residuals:} & 8 & \\textbf{ BIC: } & 218.0 \\\\\n", - "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", - "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lcccccc}\n", - " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", - "\\midrule\n", - "\\textbf{x1} & 2.2067 & 0.041 & 53.735 & 0.000 & 2.112 & 2.301 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 7.448 & \\textbf{ Durbin-Watson: } & 1.177 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.024 & \\textbf{ Jarque-Bera (JB): } & 2.533 \\\\\n", - "\\textbf{Skew:} & -1.187 & \\textbf{ Prob(JB): } & 0.282 \\\\\n", - "\\textbf{Kurtosis:} & 4.058 & \\textbf{ Cond. No. } & 1.00 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "%\\caption{WLS Regression Results}\n", - "\\end{center}\n", - "\n", - "Notes: \\newline\n", - " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", - " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " WLS Regression Results \n", - "=======================================================================================\n", - "Dep. Variable: y R-squared (uncentered): 0.997\n", - "Model: WLS Adj. R-squared (uncentered): 0.997\n", - "Method: Least Squares F-statistic: 2887.\n", - "Date: Sat, 18 Oct 2025 Prob (F-statistic): 1.60e-11\n", - "Time: 19:03:41 Log-Likelihood: -107.89\n", - "No. Observations: 9 AIC: 217.8\n", - "Df Residuals: 8 BIC: 218.0\n", - "Df Model: 1 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 2.2067 0.041 53.735 0.000 2.112 2.301\n", - "==============================================================================\n", - "Omnibus: 7.448 Durbin-Watson: 1.177\n", - "Prob(Omnibus): 0.024 Jarque-Bera (JB): 2.533\n", - "Skew: -1.187 Prob(JB): 0.282\n", - "Kurtosis: 4.058 Cond. No. 1.00\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", - "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] + "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.997 \\\\\n\\textbf{Model:} & WLS & \\textbf{ Adj. R-squared (uncentered):} & 0.997 \\\\\n\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2887. \\\\\n\\textbf{Date:} & Tue, 27 Jan 2026 & \\textbf{ Prob (F-statistic):} & 1.60e-11 \\\\\n\\textbf{Time:} & 22:16:43 & \\textbf{ Log-Likelihood: } & -107.89 \\\\\n\\textbf{No. Observations:} & 9 & \\textbf{ AIC: } & 217.8 \\\\\n\\textbf{Df Residuals:} & 8 & \\textbf{ BIC: } & 218.0 \\\\\n\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n\\midrule\n\\textbf{x1} & 2.2067 & 0.041 & 53.735 & 0.000 & 2.112 & 2.301 \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:} & 7.448 & \\textbf{ Durbin-Watson: } & 1.177 \\\\\n\\textbf{Prob(Omnibus):} & 0.024 & \\textbf{ Jarque-Bera (JB): } & 2.533 \\\\\n\\textbf{Skew:} & -1.187 & \\textbf{ Prob(JB): } & 0.282 \\\\\n\\textbf{Kurtosis:} & 4.058 & \\textbf{ Cond. No. } & 1.00 \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{WLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "y = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 1]\n", - "x = clrd_first_lags.to_frame(origin_as_datetime=True).values[:, 0]\n", - "\n", - "model = sm.WLS(y, x, weights=(1 / x) ** 2)\n", - "results = model.fit()\n", - "results.summary()" - ] + "execution_count": 7 }, { "cell_type": "markdown", @@ -436,22 +445,12 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simple average:\n", - "True\n", - "Volume-weighted average:\n", - "True\n", - "Regression average:\n", - "True\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:44.075340Z", + "start_time": "2026-01-28T04:16:44.034202Z" } - ], + }, "source": [ "print(\"Simple average:\")\n", "print(\n", @@ -488,7 +487,22 @@ " )\n", " == round(sm.OLS(y, x).fit().params[0], 10)\n", ")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple average:\n", + "True\n", + "Volume-weighted average:\n", + "True\n", + "Regression average:\n", + "True\n" + ] + } + ], + "execution_count": 8 }, { "cell_type": "markdown", @@ -499,20 +513,36 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:44.108239Z", + "start_time": "2026-01-28T04:16:44.091913Z" + } + }, "source": [ "dev = cl.Development(average=\"simple\").fit(clrd[\"CumPaidLoss\"])" - ] + ], + "outputs": [], + "execution_count": 9 }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:44.150151Z", + "start_time": "2026-01-28T04:16:44.142976Z" + } + }, + "source": [ + "dev.sigma_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 0.123197 0.034009 0.013495 0.009146 0.007386 0.006673 0.007257 0.00966 0.003222" + ], "text/html": [ "\n", " \n", @@ -544,10 +574,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 0.123197 0.034009 0.013495 0.009146 0.007386 0.006673 0.007257 0.00966 0.003222" ] }, "execution_count": 10, @@ -555,17 +581,26 @@ "output_type": "execute_result" } ], - "source": [ - "dev.sigma_" - ] + "execution_count": 10 }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:44.244837Z", + "start_time": "2026-01-28T04:16:44.238291Z" + } + }, + "source": [ + "dev.std_err_" + ], "outputs": [ { "data": { + "text/plain": [ + " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", + "(All) 0.041066 0.012024 0.005101 0.003734 0.003303 0.003337 0.00419 0.006831 0.003222" + ], "text/html": [ "\n", " \n", @@ -597,10 +632,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " 12-24 24-36 36-48 48-60 60-72 72-84 84-96 96-108 108-120\n", - "(All) 0.041066 0.012024 0.005101 0.003734 0.003303 0.003337 0.00419 0.006831 0.003222" ] }, "execution_count": 11, @@ -608,9 +639,7 @@ "output_type": "execute_result" } ], - "source": [ - "dev.std_err_" - ] + "execution_count": 11 }, { "cell_type": "markdown", @@ -621,8 +650,21 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:44.365964Z", + "start_time": "2026-01-28T04:16:44.359348Z" + } + }, + "source": [ + "np.round(\n", + " dev.sigma_.to_frame(origin_as_datetime=False).transpose()[\"(All)\"].values\n", + " / np.sqrt(\n", + " clrd[\"CumPaidLoss\"].age_to_age.to_frame(origin_as_datetime=False).count()\n", + " ).values,\n", + " 4,\n", + ")" + ], "outputs": [ { "data": { @@ -636,15 +678,7 @@ "output_type": "execute_result" } ], - "source": [ - "np.round(\n", - " dev.sigma_.to_frame(origin_as_datetime=False).transpose()[\"(All)\"].values\n", - " / np.sqrt(\n", - " clrd[\"CumPaidLoss\"].age_to_age.to_frame(origin_as_datetime=False).count()\n", - " ).values,\n", - " 4,\n", - ")" - ] + "execution_count": 12 }, { "cell_type": "markdown", @@ -657,11 +691,31 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:44.519452Z", + "start_time": "2026-01-28T04:16:44.501645Z" + } + }, + "source": [ + "clrd[\"CumPaidLoss\"]" + ], "outputs": [ { "data": { + "text/plain": [ + " 12 24 36 48 60 72 84 96 108 120\n", + "1988 285804.0 638532.0 865100.0 996363.0 1084351.0 1133188.0 1169749.0 1196917.0 1229203.0 1241715.0\n", + "1989 307720.0 684140.0 916996.0 1065674.0 1154072.0 1210479.0 1249886.0 1291512.0 1308706.0 NaN\n", + "1990 320124.0 757479.0 1017144.0 1169014.0 1258975.0 1315368.0 1368374.0 1394675.0 NaN NaN\n", + "1991 347417.0 793749.0 1053414.0 1209556.0 1307164.0 1381645.0 1414747.0 NaN NaN NaN\n", + "1992 342982.0 781402.0 1014982.0 1172915.0 1281864.0 1328801.0 NaN NaN NaN NaN\n", + "1993 342385.0 743433.0 959147.0 1113314.0 1187581.0 NaN NaN NaN NaN NaN\n", + "1994 351060.0 750392.0 993751.0 1114842.0 NaN NaN NaN NaN NaN NaN\n", + "1995 343841.0 768575.0 962081.0 NaN NaN NaN NaN NaN NaN NaN\n", + "1996 381484.0 736040.0 NaN NaN NaN NaN NaN NaN NaN NaN\n", + "1997 340132.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN" + ], "text/html": [ "\n", " \n", @@ -812,19 +866,6 @@ " \n", " \n", "
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" - ], - "text/plain": [ - " 9999\n", - "1988 NaN\n", - "1989 7312.634869\n", - "1990 17838.223062\n", - "1991 21813.683826\n", - "1992 23847.273221\n", - "1993 25282.602592\n", - "1994 28465.249566\n", - "1995 33171.832916\n", - "1996 50243.750958\n", - "1997 99026.911753" ] }, "execution_count": 23, @@ -1911,11 +2004,7 @@ "output_type": "execute_result" } ], - "source": [ - "mack.mack_std_err_[\n", - " mack.mack_std_err_.development == mack.mack_std_err_.development.max()\n", - "]" - ] + "execution_count": 23 }, { "cell_type": "markdown", @@ -1926,11 +2015,31 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:45.841342Z", + "start_time": "2026-01-28T04:16:45.828449Z" + } + }, + "source": [ + "mack.summary_" + ], "outputs": [ { "data": { + "text/plain": [ + " Latest IBNR Ultimate Mack Std Err\n", + "1988 1241715.0 NaN 1.241715e+06 NaN\n", + "1989 1308706.0 1.332126e+04 1.322027e+06 7312.634869\n", + "1990 1394675.0 4.221037e+04 1.436885e+06 17838.223062\n", + "1991 1414747.0 7.940888e+04 1.494156e+06 21813.683826\n", + "1992 1328801.0 1.197087e+05 1.448510e+06 23847.273221\n", + "1993 1187581.0 1.671916e+05 1.354773e+06 25282.602592\n", + "1994 1114842.0 2.604007e+05 1.375243e+06 28465.249566\n", + "1995 962081.0 4.024025e+05 1.364484e+06 33171.832916\n", + "1996 736040.0 6.368335e+05 1.372874e+06 50243.750958\n", + "1997 340132.0 1.056335e+06 1.396467e+06 99026.911753" + ], "text/html": [ "\n", " \n", @@ -2015,19 +2124,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " Latest IBNR Ultimate Mack Std Err\n", - "1988 1241715.0 NaN 1.241715e+06 NaN\n", - "1989 1308706.0 1.332126e+04 1.322027e+06 7312.634869\n", - "1990 1394675.0 4.221037e+04 1.436885e+06 17838.223062\n", - "1991 1414747.0 7.940888e+04 1.494156e+06 21813.683826\n", - "1992 1328801.0 1.197087e+05 1.448510e+06 23847.273221\n", - "1993 1187581.0 1.671916e+05 1.354773e+06 25282.602592\n", - "1994 1114842.0 2.604007e+05 1.375243e+06 28465.249566\n", - "1995 962081.0 4.024025e+05 1.364484e+06 33171.832916\n", - "1996 736040.0 6.368335e+05 1.372874e+06 50243.750958\n", - "1997 340132.0 1.056335e+06 1.396467e+06 99026.911753" ] }, "execution_count": 24, @@ -2035,9 +2131,7 @@ "output_type": "execute_result" } ], - "source": [ - "mack.summary_" - ] + "execution_count": 24 }, { "cell_type": "markdown", @@ -2048,8 +2142,28 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:46.070621Z", + "start_time": "2026-01-28T04:16:45.929574Z" + } + }, + "source": [ + "plt.bar(\n", + " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", + " mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", + " label=\"Paid\",\n", + ")\n", + "plt.bar(\n", + " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", + " mack.summary_.to_frame(origin_as_datetime=True)[\"IBNR\"],\n", + " bottom=mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", + " yerr=mack.summary_.to_frame(origin_as_datetime=True)[\"Mack Std Err\"],\n", + " label=\"Reserves\",\n", + ")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim(0, 1800000)" + ], "outputs": [ { "data": { @@ -2063,31 +2177,16 @@ }, { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "plt.bar(\n", - " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", - " mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", - " label=\"Paid\",\n", - ")\n", - "plt.bar(\n", - " mack.summary_.to_frame(origin_as_datetime=True).index.year,\n", - " mack.summary_.to_frame(origin_as_datetime=True)[\"IBNR\"],\n", - " bottom=mack.summary_.to_frame(origin_as_datetime=True)[\"Latest\"],\n", - " yerr=mack.summary_.to_frame(origin_as_datetime=True)[\"Mack Std Err\"],\n", - " label=\"Reserves\",\n", - ")\n", - "plt.legend(loc=\"upper left\")\n", - "plt.ylim(0, 1800000)" - ] + "execution_count": 25 }, { "cell_type": "markdown", @@ -2098,8 +2197,22 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:46.331457Z", + "start_time": "2026-01-28T04:16:46.202689Z" + } + }, + "source": [ + "ibnr_mean = mack.ibnr_.sum()\n", + "ibnr_sd = mack.total_mack_std_err_.values[0, 0]\n", + "n_trials = 10000\n", + "\n", + "np.random.seed(2021)\n", + "dist = np.random.normal(ibnr_mean, ibnr_sd, size=n_trials)\n", + "\n", + "plt.hist(dist, bins=50)" + ], "outputs": [ { "data": { @@ -2135,25 +2248,16 @@ }, { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ibnr_mean = mack.ibnr_.sum()\n", - "ibnr_sd = mack.total_mack_std_err_.values[0, 0]\n", - "n_trials = 10000\n", - "\n", - "np.random.seed(2021)\n", - "dist = np.random.normal(ibnr_mean, ibnr_sd, size=n_trials)\n", - "\n", - "plt.hist(dist, bins=50)" - ] + "execution_count": 26 }, { "cell_type": "markdown", @@ -2167,11 +2271,29 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:46.549305Z", + "start_time": "2026-01-28T04:16:46.346183Z" + } + }, + "source": [ + "samples = (\n", + " cl.BootstrapODPSample(n_sims=10000).fit(clrd[\"CumPaidLoss\"]).resampled_triangles_\n", + ")\n", + "samples" + ], "outputs": [ { "data": { + "text/plain": [ + " Triangle Summary\n", + "Valuation: 1997-12\n", + "Grain: OYDY\n", + "Shape: (10000, 1, 10, 10)\n", + "Index: [LOB]\n", + "Columns: [CumPaidLoss]" + ], "text/html": [ "\n", " \n", @@ -2203,14 +2325,6 @@ " \n", " \n", "
" - ], - "text/plain": [ - " Triangle Summary\n", - "Valuation: 1997-12\n", - "Grain: OYDY\n", - "Shape: (10000, 1, 10, 10)\n", - "Index: [LOB]\n", - "Columns: [CumPaidLoss]" ] }, "execution_count": 27, @@ -2218,12 +2332,7 @@ "output_type": "execute_result" } ], - "source": [ - "samples = (\n", - " cl.BootstrapODPSample(n_sims=10000).fit(clrd[\"CumPaidLoss\"]).resampled_triangles_\n", - ")\n", - "samples" - ] + "execution_count": 27 }, { "cell_type": "markdown", @@ -2243,20 +2352,12 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chainladder's IBNR estimate: 2777812.6890986315\n", - "BootstrapODPSample's mean IBNR estimate: 2777941.6669315123\n", - "Difference $: -128.97783288080245\n", - "Difference %: 4.643143628329176e-05\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:46.960375Z", + "start_time": "2026-01-28T04:16:46.598075Z" } - ], + }, "source": [ "ibnr_cl = cl.Chainladder().fit(clrd[\"CumPaidLoss\"]).ibnr_.sum()\n", "ibnr_bootstrap = cl.Chainladder().fit(samples).ibnr_.sum(\"origin\").mean()\n", @@ -2271,7 +2372,34 @@ ")\n", "print(\"Difference $:\", ibnr_cl - ibnr_bootstrap)\n", "print(\"Difference %:\", abs(ibnr_cl - ibnr_bootstrap) / ibnr_cl)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chainladder's IBNR estimate: 2777812.6890986315\n", + "BootstrapODPSample's mean IBNR estimate: 2779994.0713715586\n", + "Difference $: -2181.3822729270905\n", + "Difference %: 0.0007852877486980319\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/Repos/chainladder-python/chainladder/tails/base.py:120: RuntimeWarning: overflow encountered in exp\n", + " sigma_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/home/ubuntu/Repos/chainladder-python/chainladder/tails/base.py:124: RuntimeWarning: overflow encountered in exp\n", + " std_err_ = xp.exp(time_pd * reg.slope_ + reg.intercept_)\n", + "/home/ubuntu/Repos/chainladder-python/chainladder/tails/base.py:127: RuntimeWarning: invalid value encountered in multiply\n", + " sigma_ = sigma_ * 0\n", + "/home/ubuntu/Repos/chainladder-python/chainladder/tails/base.py:128: RuntimeWarning: invalid value encountered in multiply\n", + " std_err_ = std_err_* 0\n" + ] + } + ], + "execution_count": 28 }, { "cell_type": "markdown", @@ -2283,11 +2411,26 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-28T04:16:47.280565Z", + "start_time": "2026-01-28T04:16:47.015333Z" + } + }, + "source": [ + "pipe = cl.Pipeline(\n", + " steps=[(\"dev\", cl.Development(average=\"simple\")), (\"tail\", cl.TailConstant(1.05))]\n", + ")\n", + "\n", + "pipe.fit(samples)" + ], "outputs": [ { "data": { + "text/plain": [ + "Pipeline(steps=[('dev', Development(average='simple')),\n", + " ('tail', TailConstant(tail=1.05))])" + ], "text/html": [ "